Neural networks for analyzing service quality in public transportation

Garrido, Concepción Domínguez; Oña, Rocío de; Oña, Juan de

doi:10.1016/j.eswa.2014.04.045

Cited by 87 publications

(70 citation statements)

References 34 publications

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“…Over-fitting can lead to predictions that are beyond the range of the training data (Richter and Weber, 2013). To avoid model over-fitting, the collected data was randomly divided into a 70:15:15 ratio (Garrido et al, 2014;Kunt et al, 2011;Srisaeng et al, 2015). A cross validation process was carried out during the training phase to avoid over-fitting of the proposed model (Efendigil et al, 2009).…”

Section: Training and Testing The Artificial Neural Networkmentioning

confidence: 99%

“…Once the values of the training set were determined, a data testing set was fed into the model and the output compared to the target value. The model was accepted if the difference was low enough (Garrido et al, 2014). The testing set simulates the forecasting of the samples (Alekseev and Seixas, 2009).…”

Section: Training and Testing The Artificial Neural Networkmentioning

confidence: 99%

“…ANNs capture the inherent information from a considered set of variables and learn from the existing data. This occurs even when noise is present (Garrido et al, 2014). Hence, no formulation or a priori model is required (Merkus and Meesters, 2013;Watts and Worner, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…The neurons are organized into several layers and interconnected with each other through synaptic weights. Synaptic weights represent the intensity of the interaction between every pair of neurons, and the activation functions calculate the potential of every neuron (Dell´Olio et al, 2017;Garrido et al, 2014;Tiryaki and Aydın, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The most widely used type of ANN for prediction is the Multi-Layer Perceptron (MLP) model (Claveria and Torra, 2014;Garrido et al, 2014;Howard et al, 2012;Nachev, 2008). The MLP is a supervised neural network based on the original simple perceptron model.…”

Section: Introductionmentioning

confidence: 99%

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The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

2018

IJTTE

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Abstract:In this paper an Artificial Neural Network (ANN) is proposed for predicting Australia's annual export air cargo demand. The modelling in the study was based on annual data from 1993 to 2016. The ANN model was developed using the input parameters of world real merchandise exports, world population growth, world jet fuel prices, world air cargo yields (proxy for air cargo costs), outbound flights from Australia, and Australian/ United States dollar exchange rate and two dummy variables, which controlled for the strong cyclical fluctuations in air cargo demand which occurred in 2003 and 2015. The artificial neural network (ANN) used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The ANN was applied during training, testing and validation and had 8 inputs, 1 neuron in the hidden layer and 1 neuron in the output layer. The data was randomly divided into three data sets; training, testing and model validation. The best-fit model was selected according to four goodness-of-fit measures: mean absolute error (MAE), mean square error (MSE), root mean square errors (RMSE), and mean absolute percentage errors (MAPE). The highest R-value obtained from the ANN model is 0.97844. The results suggest that the ANN model is an efficient tool for predicting Australia's annual export air cargo demand.

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Section: Training and Testing The Artificial Neural Networkmentioning

confidence: 99%

Section: Training and Testing The Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

2018

IJTTE

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A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

et al. 2021

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Fault detection and diagnosis for process plants has been an active area of research for many years. This review presents a concise overview on supervised and unsupervised data‐driven approaches for fault detection and diagnosis in chemical processes. Methods based on supervised and unsupervised data‐driven techniques are reviewed, and the challenges in the field of fault detection and diagnosis have also been highlighted. It is observed that most of the data‐driven approaches are application specific, in that no single method can be used to obtain a generalized solution for nearly all purposes. The methods reviewed differ significantly from one to the other, and hence it is difficult to generalize any key similarity. The majority of the works proposed in the literature focused mainly on single fault detection, and do not cover the root‐cause diagnosis of the detected faults. In cases where both detection and diagnosis are performed, the focus is mainly for a single fault. In addition, majority of the articles do not extend to the diagnosis of the root cause for multiple and simultaneous faults.

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Determination of Service Quality of Bus Transit System Using SERVQUAL Method Based on User’s Perceptions and Expectations

Sarkar

Ahmed

Deb

2022

Lecture Notes in Civil Engineering

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Neural networks for analyzing service quality in public transportation

Cited by 87 publications

References 34 publications

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

Determination of Service Quality of Bus Transit System Using SERVQUAL Method Based on User’s Perceptions and Expectations

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